REF: de-duplicate IntervalIndex compat code (#36372)

Author: jbrockmendel

pandas/core/indexes/base.py

@@ -3316,7 +3316,7 @@ def _can_reindex(self, indexer):
         ValueError if its a duplicate axis
         """
         # trying to reindex on an axis with duplicates
-        if not self.is_unique and len(indexer):
+        if not self._index_as_unique and len(indexer):
             raise ValueError("cannot reindex from a duplicate axis")
 
     def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
@@ -3360,8 +3360,7 @@ def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
             if self.equals(target):
                 indexer = None
             else:
-                # check is_overlapping for IntervalIndex compat
-                if self.is_unique and not getattr(self, "is_overlapping", False):
+                if self._index_as_unique:
                     indexer = self.get_indexer(
                         target, method=method, limit=limit, tolerance=tolerance
                     )
@@ -4759,11 +4758,21 @@ def get_indexer_for(self, target, **kwargs):
         numpy.ndarray
             List of indices.
         """
-        if self.is_unique:
+        if self._index_as_unique:
             return self.get_indexer(target, **kwargs)
         indexer, _ = self.get_indexer_non_unique(target, **kwargs)
         return indexer
 
+    @property
+    def _index_as_unique(self):
+        """
+        Whether we should treat this as unique for the sake of
+        get_indexer vs get_indexer_non_unique.
+
+        For IntervalIndex compat.
+        """
+        return self.is_unique
+
     def _maybe_promote(self, other: "Index"):
         """
         When dealing with an object-dtype Index and a non-object Index, see

pandas/core/indexes/interval.py

@@ -516,22 +516,6 @@ def is_overlapping(self) -> bool:
         # GH 23309
         return self._engine.is_overlapping
 
-    def _can_reindex(self, indexer: np.ndarray) -> None:
-        """
-        Check if we are allowing reindexing with this particular indexer.
-
-        Parameters
-        ----------
-        indexer : an integer indexer
-
-        Raises
-        ------
-        ValueError if its a duplicate axis
-        """
-        # trying to reindex on an axis with duplicates
-        if self.is_overlapping and len(indexer):
-            raise ValueError("cannot reindex from an overlapping axis")
-
     def _needs_i8_conversion(self, key) -> bool:
         """
         Check if a given key needs i8 conversion. Conversion is necessary for
@@ -839,21 +823,9 @@ def get_indexer_non_unique(
 
         return ensure_platform_int(indexer), ensure_platform_int(missing)
 
-    def get_indexer_for(self, target: AnyArrayLike, **kwargs) -> np.ndarray:
-        """
-        Guaranteed return of an indexer even when overlapping.
-
-        This dispatches to get_indexer or get_indexer_non_unique
-        as appropriate.
-
-        Returns
-        -------
-        numpy.ndarray
-            List of indices.
-        """
-        if self.is_overlapping:
-            return self.get_indexer_non_unique(target)[0]
-        return self.get_indexer(target, **kwargs)
+    @property
+    def _index_as_unique(self):
+        return not self.is_overlapping
 
     def _convert_slice_indexer(self, key: slice, kind: str):
         if not (key.step is None or key.step == 1):

pandas/core/indexing.py

@@ -1256,7 +1256,7 @@ def _get_listlike_indexer(self, key, axis: int, raise_missing: bool = False):
             )
             return ax[indexer], indexer
 
-        if ax.is_unique and not getattr(ax, "is_overlapping", False):
+        if ax._index_as_unique:
             indexer = ax.get_indexer_for(keyarr)
             keyarr = ax.reindex(keyarr)[0]
         else: